Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data
Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-04-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2220-9964/7/4/158 |
| _version_ | 1818292351533056000 |
|---|---|
| author | Shanshan Han Fu Ren Chao Wu Ying Chen Qingyun Du Xinyue Ye |
| author_facet | Shanshan Han Fu Ren Chao Wu Ying Chen Qingyun Du Xinyue Ye |
| author_sort | Shanshan Han |
| collection | DOAJ |
| description | Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field. |
| first_indexed | 2024-12-13T02:58:35Z |
| format | Article |
| id | doaj.art-e178b0601ba04c63b966b331683dba69 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-13T02:58:35Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-e178b0601ba04c63b966b331683dba692022-12-22T00:01:54ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-04-017415810.3390/ijgi7040158ijgi7040158Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in DataShanshan Han0Fu Ren1Chao Wu2Ying Chen3Qingyun Du4Xinyue Ye5School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaDepartment of Geography, Kent State University, Kent, OH 44242, USAOver the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.http://www.mdpi.com/2220-9964/7/4/158check-in datavisitor behavioursdeep neural networkTensorFlowHong Kong |
| spellingShingle | Shanshan Han Fu Ren Chao Wu Ying Chen Qingyun Du Xinyue Ye Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data ISPRS International Journal of Geo-Information check-in data visitor behaviours deep neural network TensorFlow Hong Kong |
| title | Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data |
| title_full | Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data |
| title_fullStr | Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data |
| title_full_unstemmed | Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data |
| title_short | Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data |
| title_sort | using the tensorflow deep neural network to classify mainland china visitor behaviours in hong kong from check in data |
| topic | check-in data visitor behaviours deep neural network TensorFlow Hong Kong |
| url | http://www.mdpi.com/2220-9964/7/4/158 |
| work_keys_str_mv | AT shanshanhan usingthetensorflowdeepneuralnetworktoclassifymainlandchinavisitorbehavioursinhongkongfromcheckindata AT furen usingthetensorflowdeepneuralnetworktoclassifymainlandchinavisitorbehavioursinhongkongfromcheckindata AT chaowu usingthetensorflowdeepneuralnetworktoclassifymainlandchinavisitorbehavioursinhongkongfromcheckindata AT yingchen usingthetensorflowdeepneuralnetworktoclassifymainlandchinavisitorbehavioursinhongkongfromcheckindata AT qingyundu usingthetensorflowdeepneuralnetworktoclassifymainlandchinavisitorbehavioursinhongkongfromcheckindata AT xinyueye usingthetensorflowdeepneuralnetworktoclassifymainlandchinavisitorbehavioursinhongkongfromcheckindata |